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The Imaginary Healthy Patient

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Abstract

Anxiety and depression may have serious disabling consequences for health, social, and occupational outcomes for people who are unaware of their actual health status and/or whose mental health symptoms remain undiagnosed by physicians. This article provides a big picture of unrecognised anxiety and depressive troubles revealed by a low score on the Mental Health Inventory-5 (MHI-5) with the help of machine learning methods using the 2012 French National Representative Health and Social Protection Survey (Enquête Santé et Protection Sociale, ESPS) matched with yearly healthcare consumption data from the French Sickness Fund. Compared to people with no latent symptoms who did not declare any depression over the last 12 months, those with unrecognised anxiety or depression were found to be older, more deprived, more socially disengaged, at a higher probability of adverse working conditions, and with higher healthcare expenditures backed, to some extent, by chronic conditions other than anxiety or mood disorder.

Suggested Citation

  • Amady Seydou Ba & Ewen Gallic & Pierre Michel & Alain Paraponaris, 2024. "The Imaginary Healthy Patient," AMSE Working Papers 2435, Aix-Marseille School of Economics, France.
    • Handle: RePEc:aim:wpaimx:2435
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    File URL: https://www.amse-aixmarseille.fr/sites/default/files/working_papers/wp_2024_-_nr_35.pdf
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    More about this item

    Keywords

    unrecognised mental disorders; mental health inventory-5 (MHI-5); healthcare consumption; workplace outcomes; tree-based methods; SHAP values;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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